In this article, I’ll explain how I estimated how many additional wins an established quarterback can bring to new team by forecasting his likely effect on team passing stats.

First, we need an idea of which stats the quarterback on an NFL team is primarily responsible for. Team passing performance is dependent on every member of the offense, so it is very difficult to parse what can attributed to the quarterback and what can be attributed to other players. We’ll need to make some assumptions from a qualitative look at the NFL records.

These assumptions are only rough estimations of the stats we need to estimate wins. At this point, it would be impossible to declare exactly how much responsibility a QB has for certain team stats, and even if we could it probably varies from team to team. I’m only going to use them to get a back-of-the-envelope estimation for the effect of acquiring a veteran QB. Assuming all other things stay the same, would he add 5 wins, 2 wins, or none?

PROJECTIONS

Completion percentage appears to be entirely owned by the QB. Year-to-year deviations in completion % tend to be very narrow. QB careers are typically marked by one low percentage rookie year, followed by a steady completion rate in subsequent years. Team changes do not affect completion % any more than the small year-to-year variations while playing for the same team. We’ll say that the QB owns completion percentage.

Yards per catch varies significantly from year-to-year. It seems to be far more dependent on the QB’s supporting cast than completion %. My rough estimate is that the QB is responsible for 1/3 of the yards per catch stat. The other 2/3 is most likely due to receiver performance, and yards-after-catch in particular. Another factor might be the type of offensive scheme used by his offense. We’ll say that the QB owns 1/3 of yards per catch and the other 2/3 is owned by the team’s receivers or other factors.

(Yards per catch)*(completion %) = yards per attempt, the primary pass stat used in the win estimation model. So we can already approximate it.

We also want to consider sacks. Sack yards are generally due to a combination of three things: QB immobility/poor decision, pass protection failure, or a failure of the receivers to get open/run the correct route. QB sacks do appear to vary significantly from year-to-year, suggesting a quarterback’s share of responsibility for his sacks is no more than 50%. We’ll say that 1/2 of a QB’s sack rate is owned by the QB himself.

The last stat we need to estimate is interception rate. Total interceptions vary from year to year, but interceptions per pass attempt is much steadier. And although the cause of an interception can be contributed to by a receiver breaking the wrong way or a QB being hurried by a pass rusher, it is the QB’s ultimate decision to throw the ball. There are also freak interceptions, such as bobbled passes, but we’ll assume those types of plays tend to even out among QBs over time. Ultimately, the decision to throw and the location of the pass rests on his ability alone. We’ll say the QB completely owns his interception rate.

We can use two recent cases of veteran QBs coming to new teams to see if the assumptions and projections make sense. Both Drew Brees and Steve McNair had significant impacts on their new teams.

DREW BREES IN NEW ORLEANS

Averaging Brees’s last 4 (healthy) seasons at San Diego as a baseline, his typical performance yields the following stats. (Notes: YPA does not yet factor in sack data. Sack rate is defined as sack yards per pass attempt.)

Brees had a career year, and our projections are sure to undervalue his contribution to NO’s improved record. By using a win model based on efficiency, we can compare the projected wins “added” by Brees and the actual wins added. (Sack data is now factored in to pass efficiency.)

How to read the chart: The first data column lists the actual efficiency stats of NO in 2005. The expected wins given their stats in ’05 was 4.7 wins, although they only won 3. The second column lists the efficiency stats we would project with the assumptions discussed above. The third column lists the actual stats for NO in ’06. The next column, titled +QB wins, indicates how many added wins our projections would attribute to the arrival of Drew Brees in NO. The final column lists the actual added wins attributed to the actual performance of the entire Saints team in 2006.

In the case of Brees, the assumptions would have underestimated his impact. Brees had a career year in ’06. The projection would have estimated +2.0 added wins attributed to him. In actuality, his contribution was almost double the projection at +3.9 added wins. Other team improvements added another +2.7 wins.

I think the primary reason for the disparity is the arrival of receivers Hopper and Colston, and an amazing 23.3 yards/catch from 2nd year receiver Henderson. I’m sure Brees had a lot to do with their success, but his new receivers’ success would have been impossible to predict. At the very least, our projections can say how many wins a quarterback could add to a new team, given that he has a career year--a best case scenario.

STEVE MCNAIR IN BALTIMORE

Averaging McNair’s last four (healthy) seasons in Tennessee, we get this baseline:

McNair was kind enough to match our projections much better than Brees. Unlike Brees, McNair was working with a receiving corps in Baltimore that was unchanged from ’05 to ’06. The biggest difference between the projections and the actual stats was in the sack rates.

We would have predicted that McNair would bring +1.8 added wins to the Ravens in ’06. He actually performed well enough to contribute +2.3 added wins, but “about 2 wins” is good enough for the back of the envelope. The biggest difference for the Ravens between ’05 and ’06 was in the defense’s interception rate which added +2.7 wins to the bottom line.

Although the projections are well in line with the actual results in ’06, we still may be discounting McNair’s presence. The difference between the 10.2 projected wins and the 13 actual wins might be due to his three game-winning 4th quarter drives against SD, CLE, and TEN. All three games appeared out of reach to Baltimore fans who were accustomed to the pre-McNair passing offense.

RULES OF THUMB

When I was a fighter pilot, I loved rules of thumb. At the speed of sound I didn't have time for a lot of math, so quick, simple, but accurate estimates helped prevent me from running out of gas. Here are a couple quick and simple rules for estimating a quarterback's statistical impact on his team's wins.

Completion percentage directly affects our model's most important statistic, pass yards per attempt. Using the league average yard per completion average of 11.5, every point of completion percentage adds 0.115 yds/att. (11.5 * 0.01 = 0.115). Every 0.115 yds/att added to pass efficiency adds 0.18 expected wins. About a 5 point increase would be needed for an extra win based on completion percentage alone.

Interceptions are also critical. Based on the league average of 519 pass attempts, every interception increases the interception rate by 1/519 = 0.18%. In turn, every 0.18% increase in interception rate reduces expected team wins by 0.11 wins. About every 10 interceptions throughout the season results in 1 fewer expected win.

In the next post I will apply the projection model onto the Miami Dolphins' new quarterback, Trent Green.

3 Responses to “New QB Added Wins”

In the Trent Green analysis, how will you treat Green's post-concussion performance? Will you project based off of his 2005 stats since that was his last full season or just use projected 2006 stats to project 2007 performance?

@BBurkeESPN

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